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This paper reports on the results of the Zero Resource Speech Challenge 2015, the first unified benchmark for zero resource speech technology, which aims at the unsupervised discovery of subword and word units from raw speech. This paper discusses the motivation for the challenge, its data sets, tasks and baseline systems. We outline the ideas behind the systems that were submitted for the two challenge tracks: unsupervised subword unit modeling and spoken term discovery, and summarize their results. The results obtained by participating teams show great promise; many systems beat the provided baselines and some even perform better than comparable supervised systems. [ABSTRACT FROM AUTHOR]

The exploration problem has been extensively studied in unsafe networks containing malicious hosts of a highly harmful nature, called black holes , which completely destroy mobile agents that visit them. In a recent work, Královič and Miklík (SIROCCO 2010, LNCS 6058, pp. 157–167) [20] considered various types of malicious host behavior in the context of the Periodic Data Retrieval problem in asynchronous ring networks with exactly one malicious host. In this problem, a team of initially co-located agents must report data from all safe nodes of the network to the homebase, infinitely often. The malicious host can choose whether to kill visiting agents or allow them to pass through (gray hole). In another variation of the model, the malicious host can, in addition, alter its whiteboard contents in order to deceive visiting agents. The goal is to design a protocol for Periodic Data Retrieval using as few agents as possible. In this paper, we present the first nontrivial lower bounds on the number of agents for Periodic Data Retrieval in asynchronous ring networks. Specifically, we show that at least 4 agents are needed when the malicious host is a gray hole, and at least 5 agents are needed when the malicious host whiteboard is unreliable. This improves the previous lower bound of 3 in both cases and answers an open question posed in the aforementioned paper. On the positive side, we propose an optimal protocol for Periodic Data Retrieval in asynchronous rings with a gray hole, which solves the problem with only 4 agents. This improves the previous upper bound of 9 agents and settles the question of the optimal number of agents in the gray-hole case. Finally, we propose a protocol with 7 agents when the whiteboard of the malicious host is unreliable, significantly improving the previously known upper bound of 27 agents. Along the way, we set forth a detailed framework for studying networks with malicious hosts of varying capabilities. [ABSTRACT FROM AUTHOR]

Sensitivity analysis is an important issue in large-scale mathematical modelling. We developed a novel 3-stage method for global sensitivity analysis of the Unified Danish Eulerian Model (UNI-DEM). This is a powerful large-scale air pollution model with an up-to-date high-performance software implementation. There is a number of uncertain internal parameters, especially in the chemistry–emission submodel, which are subject to our quantitative sensitivity study. Efficient Monte Carlo and quasi-Monte Carlo algorithms based on Sobol sequences are used in this study. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A vast number of numerical experiments were carried out with SA-DEM on an IBM Blue Gene/P, the most powerful parallel supercomputer, at the time of the write-up of this paper, in Bulgaria. Even this powerful machine has some problems with the storage when SA-DEM is to be run with the refined (480×480) version of the mesh. The code was implemented with some enhancements on the IBM MareNostrum III at BSC — Barcelona, the most powerful parallel supercomputer in Spain. This implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results and performance analysis of these results will be presented in the paper. [ABSTRACT FROM AUTHOR]

The paper deals with the studies of the forced impacting oscillator when taking into account the dry and viscous resistance as well as the generalized Hertz contact law during an impact. Numerical investigations of the mathematical model are accompanied by validations obtained with an experimental rig. To study the solutions of the mathematical model, the sequences of impacts, when the system evolves in periodic and chaotic modes, is used. The statistical properties of chaotic impact events are considered in detail. In particular, successive iterations of the impact map, autocorrelation function and coefficient of variation for the impact train, histograms for the inter-impact intervals and values of obstacle penetrations are analyzed. It is revealed that the impact sequence is stationary but non-Poissonian and contains temporal scales that do not relate to the external stimulus. This sequence can be described by a bimodal distribution. The findings are confirmed by the analysis of experimental data. • Mathematical model and experimental stand of a forced impacting oscillator are studied. • Impact event sequences are considered numerically and experimentally. • Statistical properties of event sequences are examined. • Agreement between properties of numerical and experimental model is shown. [ABSTRACT FROM AUTHOR]

It is an intuitive assumption that some activities require more energy than others. Bottom-up energy demand models therefore use time-use data to inform the timing of energy use. In this paper we present some empirical evidence to test the strength of this assumption. Using data that simultaneously captures household activities and their coinciding electricity consumption, it is possible to relate one to the other. We validate the temporal accuracy of the approach with the example of reporting hot drinks and the distinct signature of kettle usage. Despite good data accuracy, the predictive power of reported activities for electricity use is modest. At time when activities that would subjectively be associated with high energy consumption are reported, electricity use is only about 7% higher than at times with activities of low energy association. For single occupant households the link is stronger with more than 30% difference between the two activity categories. We conclude that demand models may need to take account of diversity and complexity in multi-occupant households and that more sophisticated regression techniques may be required to improve demand predictions based on time-use data. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) is a valuable tool for analyzing the building stock. Many different model approaches have been proposed in recent years suggesting various ways of dealing with the challenges of UBEM; however, central for all modeling approaches is the need for informative input data about the building stock. The availability of data for urban-scale modeling is both country-specific, time-consuming to aggregate, and data access is often limited due to privacy constraints. In this paper, we present a hierarchical bottom-up model of the Danish residential building stock using public building data for predicting the annual heating energy consumption. For more than 10,000 randomly selected single-family dwellings, the annual energy consumption is modeled and validated for the city of Aarhus, Denmark. We found that approx. 50% of the energy use is explained using only four widely available building characteristics, which enables building-scale predictions with a mean absolute error of approx. 25%. In addition, for city-scale predictions, the regression-based model enables aggregated predictions with a mean bias error of less than ±2%. Even though building-scale predictions are only somewhat accurate, the performance remains comparable to state-of-the art high-fidelity models in the literature. [ABSTRACT FROM AUTHOR]

Data reconciliation is a crucial technique to improve the accuracy of the measured data in industrial processes. However, most traditional data reconciliation researches mainly focused on global modeling for single mode processes, but little attention was paid to multimode processes. In this paper, a layered online data reconciliation strategy based on Gaussian mixture model is proposed for complex industrial processes with multiple modes. In the proposed data reconciliation framework, Gaussian mixture model is first used to identify and partition different operating modes from process data. Then, layered data reconciliation models are established for each operating mode. In the online data reconciliation step for new data, it is reconciled with the trained reconciliation models from different modes and its posteriors corresponding to different modes are calculated for new data. Finally, the reconciled result is obtained by the weighted sum of individual reconciled data in each operating mode. The effectiveness and feasibility of the proposed data reconciliation strategy are validated through a real industrial application on the sodium aluminate solution evaporation process. [ABSTRACT FROM AUTHOR]

Mining interesting itemsets with both high support and utility values from transactional database is an important task in data mining. In this paper, we consider the two measures support and utility in a unified framework from a multi-objective view. Specifically, the task of mining frequent and high utility itemsets is modeled as a multi-objective problem. Then, a multi-objective itemset mining algorithm is proposed for solving the transformed problem, which can provide multiple itemsets recommendation for decision-makers in only one run. One key advantage of the proposed multi-objective algorithm is that it does not need to specify the prior parameters such as minimal support threshold min_sup and minimal utility threshold min_uti , which brings much convenience to users. The experimental results on several real datasets demonstrate the effectiveness of the proposed algorithm. In addition, comparison results show that the proposed algorithm can provide more diverse yet frequent and high utility itemsets in only one run. [ABSTRACT FROM AUTHOR]

Input and output data, under uncertainty, must be taken into account as an essential part of data envelopment analysis (DEA) models in practice. Many researchers have dealt with this kind of problem using fuzzy approaches, DEA models with interval data or probabilistic models. This paper presents an approach to scenario-based robust optimization for conventional DEA models. To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates. The robust DEA model proposed is aimed at ranking decision-making units (DMUs) based on their sensitivity analysis within the given set of scenarios, considering both feasibility and optimality factors in the objective function. The model is based on the technique proposed by Mulvey et al. (1995) for solving stochastic optimization problems. The effect of DMUs on the product possibility set is calculated using the Monte Carlo method in order to extract weights for feasibility and optimality factors in the goal programming model. The approach proposed is illustrated and verified by a case study of an engineering company. [ABSTRACT FROM AUTHOR]

Hierarchical Linear Mixed Model (HLMM) is an extension of Linear Mixed Model with hierarchical levels of observation. HLMM allows researchers to model different types of covariance structure to describe data properly while in classical linear model the covariance structure defines in constant variance and correlation that hardly applicable for longitudinal data. This paper describes two levels HLMM which represents a single growth curves model. Level-1 presents growth shape to capture within-subject effect and level-2 presents growth parameters that characterized between-subject differences. We model the covariance structure of level-1 random effect to excavate individual growth performance and applied to longitudinal data from poverty data of 34 provinces in Indonesia. Different types of covariance structures are modeled using PROC MIXED in SAS system, produce that AR(1) is the alternative of constant covariance structure and ARH(1) as an alternative for non-constant variance structure based on -2RLL, AIC and BIC criteria. [ABSTRACT FROM AUTHOR]

This paper proposes a research framework for evaluating the relationship between motivation and owner commitment for improving the delivery performance of green building projects in the construction industry. The research framework can generate knowledge in a positivistic manner, and the quantitative methodology for planning and devising the survey method of data collection and the structural equation modelling technique of data analysis. Theoretically, the research framework can be modified or extended by other researchers for investigating the delivery performance of green building projects, while in practical terms, it will help in identifying how to increase project owners’ commitment for improving the delivery performance of green building projects in the construction industry. Finally, a research agenda is proposed towards achieving the aim of establishing empirical relationship between motivation and owner commitment for improving the delivery performance of green building projects. [ABSTRACT FROM AUTHOR]

This paper revisits the Boolean logical requirement of a pattern and develops 0-1 multilinear programming (MP) models for (Pareto-)optimal patterns for logical analysis of data (LAD). We show that all existing and also new pattern generation models can naturally be obtained from the MP models via linearization techniques for 0-1 multilinear functions. Furthermore, 0-1 MP provides an insight for understanding how different and independently developed models for a particular type of pattern are inter-related. These show that 0-1 MP presents a unifying theory for pattern generation in LAD. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the parameters of a complex model representing an industrial scale polymerization process. The estimability analysis of the parameters prior to estimation allows simplifying the optimization problem but it is usually neglected in literature when industrial data is used for estimation. In this case, though, the estimability analysis would be even more important since usually less data is available, they are associated with a higher uncertainty and the experiments might not be designed as in laboratory or pilot plant. The orthogonalization method reduced from 68 to 29 the number of parameters of the model. Polymer properties, which are measured offline with low frequency, as well as process temperatures and flow rates are used for validating the model. Small deviations, up to 5%, between model prediction and experimental data indicate the quality of fit of the model and the importance of carrying out first an estimability analysis. [ABSTRACT FROM AUTHOR]

In recent years, we have observed a significant trend towards filling the gap between social network analysis and control. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the advancement in complex networks theory and multi-agent systems, and the development of modern computational tools for big data analysis. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with applications to social systems, to the attention of the broad research community. This paper is the first part of the tutorial, and it is focused on the most classical models of social dynamics and on their relations to the recent achievements in multi-agent systems. [ABSTRACT FROM AUTHOR]

Aquatic biogeochemical models are vital tools in understanding and predicting human impacts on water clarity. In this paper, we develop a spectrally-resolved optical model that produces remote-sensing reflectance as a function of depth-resolved biogeochemical model properties such as phytoplankton biomass, suspended sediment concentrations and benthic reflectance. We compare simulated remote-sensing reflectance from a 4 km resolution coupled hydrodynamic, optical, sediment and biogeochemical model configured for the Great Barrier Reef with observed remote-sensing reflectance from the MODIS sensor at the 8 ocean colour bands. The optical model is sufficiently accurate to capture the remote-sensing reflectance that would arise from a specific biogeochemical state. Thus the mismatch between simulated and observed remote-sensing reflectance provides an excellent metric for model assessment of the coupled biogeochemical model. Finally, we combine simulated remote-sensing reflectance in a red/green/blue colour model to produce simulated true colour images during the passage of Tropical Cyclone Yasi in February 2011. [ABSTRACT FROM AUTHOR]

This paper presents a novel, simple method for reducing external operating condition datasets to be used in multi-generation system optimization models. The method, called the Characteristic Operating Pattern (CHOP) method, is a visually-based aggregation method that clusters reference data based on parameter values rather than time of occurrence, thereby preserving important information on short-term relations between the relevant operating parameters. This is opposed to commonly used methods where data are averaged over chronological periods (months or years), and extreme conditions are hidden in the averaged values. The CHOP method is tested in a case study where the operation of a fictive Danish combined heat and power plant is optimized over a historical 5-year period. The optimization model is solved using the full external operating condition dataset, a reduced dataset obtained using the CHOP method, a monthly-averaged dataset, a yearly-averaged dataset, and a seasonal peak/off-peak averaged dataset. The economic result obtained using the CHOP-reduced dataset is significantly more accurate than that obtained using any of the other reduced datasets, while the calculation time is similar to those obtained using the monthly averaged and seasonal peak/off-peak averaged datasets. The outcomes of the study suggest that the CHOP method is advantageous compared to chronology-averaging methods in reducing external operating condition datasets to be used in the design optimization models of flexible multi-generation systems. [ABSTRACT FROM AUTHOR]

The thermal gas mass flow meter is an important meter used in industrial measurement. When the environmental temperature changes, the measured gas physical parameters change correspondingly and the thermal gas mass flow meter output signal is affected, causing large measurement error. The influence of gas temperature on the sheathed probe measurements is analyzed in this paper based on experiments and heat transfer theory using a three dimensional probe and gas heat transfer mathematical model based on the heat conduction equation. The probe heat transfer process is analyzed under convection heat transfer coupling conditions. The experimental data were analyzed and compared against the theoretical results, with a maximum average relative error of only 4.56%. The rationality of the theoretical method is thus verified. [ABSTRACT FROM AUTHOR]

In this paper we consider relational T -algebras, objects in ( T , 2 ) - Cat , as spaces and we explore the topological property of T -regularity. This notion goes back to Möbus [18] who introduced it in a more general abstract framework. When applied to the ultrafilter monad and to the well known lax-algebraic presentation of Top as ( , 2 ) - Cat , -regularity is known to be equivalent to the usual regularity of the topological space [5] . We prove that in general for a power-enriched monad T with the Kleisli extension, even when restricting to proper elements, T -regularity is too strong since in most cases it implies the object being indiscrete. For the lax-algebraic presentations of Top as ( F , 2 ) - Cat , via the power-enriched filter monad F and of App as ( I , 2 ) - Cat , via the power-enriched functional ideal monad I , we present weaker conditions in terms of convergence of filters and functional ideals respectively, equivalent to the usual regularity in Top and App . For the lax-algebraic presentation of App as ( B , 2 ) - Cat , via the prime functional ideal monad B , a submonad of I with the initial extension to Rel , restricting to proper elements already gives more interesting results. We prove that B -regularity (restricted to proper prime functional ideals) is equivalent to the approach space being topological and regular. However it requires further weakening of the concept to obtain a characterization of the usual regularity in App in terms of convergence of prime functional ideals. [ABSTRACT FROM AUTHOR]

This paper studies injectivity for continuous maps between T0-spaces. The new characterizations obtained establish a parallelism between characterizations of injective monotone maps between ordered sets and of injective continuous maps between T0-spaces. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight ‘Plugged-in Places’ projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data. A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained. The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle swarm optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation. [ABSTRACT FROM AUTHOR]

Business archetypes and archetype patterns were originally introduced by Arlow and Neustadt. Business archetype patterns (Product, Party, Order, Inventory, Quantity and Rule), composed of business archetypes (e.g., Person's Name, Address, Phone Number) are the universal information models describing the universe of discourse of businesses. We have redesigned these archetypes and archetype patterns and are using the redesigned version of archetypes and archetype patterns in the development of real life healthcare software. In this paper we compare the redesigned archetypes and archetype patterns with the HL7 Version 3: Reference Information Model and openEHR Reference Model. In our comparison we emphasize semantical aggregation of health data across heterogeneous data sources, as well as the interoperability and evolution of models and software. [ABSTRACT FROM AUTHOR]

More and more asset management organizations are relying on trusted data to drive their decision making process. Yet little systematic research has been performed regarding the generation of trusted data. Trusted data has a clear, defined and consistent quality which meets the expectations of the user. The aim of the research presented in this paper is to develop a conceptual model to support asset management organizations with regards to their development of data infrastructures which produce trusted data and provide organizations with the capability to make the right decisions at the right time. The autonomous characteristics of agent-based systems and process orientation of discrete-event simulation are combined in our conceptual model. In this way both the autonomous social behavior of organizations and their business processes can be modelled. Several predefined components containing the behavior of agents through various coordination mechanisms have been developed. These components can be further customized to model an empirical situation more closely. The approach is illustrated with a case study in the asset management domain. [ABSTRACT FROM AUTHOR]

The paper presents an early-stage research which is aimed towards the development of comprehensive conceptual and technological framework for ensemble-based simulation of complex systems. The concept of multi-layer ensemble is presented as a background for further development of the framework to cover different kind of ensembles: ensemble of system's state, data ensemble, and models ensemble. Formal description of a hybrid model is provided as a core concept for ensemble-based complex system simulation. The example of water level forecasting application is used to show selected ensemble classes covered by the proposed framework. [ABSTRACT FROM AUTHOR]

Exploration of tissue sections by imaging mass spectrometry reveals abundance of different biomolecular ions in different sample spots, allowing finding region specific features. In this paper we present computational and statistical methods for investigation of protein biomarkers i.e. biological features related to presence of different pathological states. Proposed complete processing pipeline includes data pre-processing, detection and quantification of peaks by using Gaussian mixture modeling and identification of specific features for different tissue regions by performing permutation tests. Application of created methodology provides detection of proteins/peptides with concentration levels specific for tumor area, normal epithelium, muscle or saliva gland regions with high confidence. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient and automated scheme to predict and identify the position and motion errors of rotary axes on a non-orthogonal five-axis machining centre using the double ball bar (DBB) system. Based on the Denavit-Hartenberg theory, a motion deviations model for the tilting rotary axis B and rotary C of a non-orthogonal five-axis NC machine tool is established, which considers tilting rotary axis B and rotary C static deviations and dynamic deviations that total 24. After analysing the mathematical expression of the motion deviations model, the QC20 double ball bar (DBB) from the Renishaw Company is used to measure and identify the motion errors of rotary axes B and C, and a measurement scheme is designed. With the measured results, the 24 geometric deviations of rotary axes B and C can be identified intuitively and efficiently. This method provides a reference for the error identification of the non-orthogonal five-axis NC machine tool. [ABSTRACT FROM AUTHOR]

In order to define systems enabling the automatic identification of occurring situations, numerous approaches employing intelligent software agents to analyse data coming from deployed sensors have been proposed. Thus, it is possible that more agents are committed to monitor the same phenomenon in the same environment. Redundancy of sensors and agents is needed, for instance, in real world applications in order to mitigate the risk of faults and threats. One of the possible side effects produced by redundancy is that agents, observing the same phenomenon, could provide discordant opinions. Indeed, solid mechanisms for reaching an agreement among these agents and produce a shared consensus on the same observations are needed. This paper proposes an approach to integrate a fuzzy-based consensus model into a Situation Awareness framework. The main idea is to consider intelligent agents as experts claiming their opinions (preferences) on a phenomenon of interest. [ABSTRACT FROM AUTHOR]

Average surface roughness value ( R a ) is an important measure of the quality of a machined work piece. Lower the R a value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal R a value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum R a value. Two techniques, model tree and sequential minimal optimization based support vector machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum R a value. Results indicate that both techniques reduced the minimum R a value of experimental data by 4.2% and 2.1% respectively. Model trees are found to be better than other approaches in predicting minimum R a value. [ABSTRACT FROM AUTHOR]

The paper is devoted to the problem of fault detection in technical systems described by nonlinear dynamical models containing non-smooth nonlinearities. So-called “model-free” or “data-driven” method is used to solve the problem. The feature of this method is that parameters of the system under consideration may be unknown. The algebra of functions is used to solve the problem under consideration. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of distributed job shop scheduling in which the classical single-facility job shop is extended to the multi-facility one. The mathematical formulation of the problem is comprehensively discussed. Two different mixed integer linear programming models in form of sequence and position based variables are proposed. Using commercial software of CPLEX, the small sized problems are optimally solved. To solve large sized problems, besides adapting three well-known heuristics, three greedy heuristics are developed. The basic idea behind the developed heuristics is to iteratively insert operations (one at each iteration) into a sequence to build up a complete permutation of operations. The permutation scheme, although having several advantages, suffers from redundancy which is having many different permutations representing the same schedule. The issue is analyzed to recognize the redundant permutation. That improves efficiency of heuristics. Comprehensive experiments are conducted to evaluate the performance of the two models and the six heuristics. The results show sequence based model and greedy heuristics equipped with redundancy exclusion are effective for the problem. [ABSTRACT FROM AUTHOR]

In an inter-organizational setting the manual construction of process models is challenging because the different people involved have to put together their partial knowledge about the overall process. Process mining, an automated technique to discover and analyze process models, can facilitate the construction of inter-organizational process models. This paper presents a technique to merge the input data of the different partners of an inter-organizational process in order to serve as input for process mining algorithms. The technique consists of a method for configuring and executing the merge and an algorithm that searches for links between the data of the different partners and that suggests rules to the user on how to merge the data. Tool support is provided in the open source process mining framework ProM. The method and the algorithm are tested using two artificial and three real life datasets that confirm their effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance. [ABSTRACT FROM AUTHOR]

Abstract: Open-circuit-voltage (OCV) data is widely used for characterizing battery properties under different conditions. It contains important information that can help to identify battery state-of-charge (SOC) and state-of-health (SOH). While various OCV models have been developed for battery SOC estimation, few have been designed for SOH monitoring. In this paper, we propose a unified OCV model that can be applied for both SOC estimation and SOH monitoring. Improvements in SOC estimation using the new model compared to other existing models are demonstrated. Moreover, it is shown that the proposed OCV model can be used to perform battery SOH monitoring as it effectively captures aging information based on incremental capacity analysis (ICA). Parametric analysis and model complexity reduction are also addressed. Experimental data is used to illustrate the effectiveness of the model and its simplified version in the application context of SOC estimation and SOH monitoring. [Copyright &y& Elsevier]

Abstract: This paper presents a hybrid air handling unit (AHU) fault detection strategy based on Principal Component Analysis (PCA) method and Pattern Matching method. The basic idea of the pattern matching method is to locate periods of operation from a historical data set whose operational conditions are similar to the target operating condition. The proposed Pattern Matching-PCA method uses two similarity factors, PCA similarity factors and Distance similarity factors, to characterize the degree of similarity between historical data window and current snapshot data. PCA model is then built using the historical AHU operation dataset that are identified to be similar to current snapshot operation data. The method is validated by operational data of an AHU system in real building. The results show that the sensibility of PCA models is enhanced by preprocessing the training data with the Pattern Matching method. [Copyright &y& Elsevier]

Process simulation (PS) is essential in the eco-efficiency assessments (EEA) of dairy processes. PS indeed allows for analysis of a process with the help of mathematical models, and its ability to calculate rapidly and at little cost the mass and energy balances of processes can be exploited in the EEA of dairy processes. This paper presents the prototype of a workable tool developed to carry-out EEA of dairy processes. A process modelling and simulation software dedicated to dairy processing was developed to perform inventories of material and energy flows (IMEF) on the processes modelled by the software user. Addition of impact analysis data sets allowed for quantification of the potential environmental impacts associated with the modelled processes. Inputting economic data then made it possible to calculate the economic value associated with the modelled processes. Gathering these methods within a single numerical tool enabled EEA of any process modelled by the software user, but also identification of the hot spots and the opportunities for eco-efficiency improvements, comparisons of several scenarios of raw milk valuation, and assessment of their economic viability. In order to present the operational aspect of the developed prototype, it was used to compare the EE of fluid milk processing in Canada (Quebec) and the United-States of America. Individual eco-efficiency indicators (EEI) results showed that the potential environmental impacts were higher for the American scenario in three damage categories out of four. Economic viability results showed a slight advantage for the American scenario. Considering all the EEI, the comparative EEA did not offer evidence of a general benefit of one scenario over the other. Image 107496 • Process simulation can make eco-efficiency assessments easier and faster. • A numerical tool for eco-efficiency assessment in dairy processing was developed. • Such tool allows for identification of hot spots and opportunities for improvements. • Eco-efficiency of all raw milk valuation scenarios can be compared in this way. • Profitability and rentability threshold of modelled processes can also be assessed. [ABSTRACT FROM AUTHOR]